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We confirmed that monolayer water confined by parallel graphene sheets spontaneously crystallizes from a structurally and dynamically heterogeneous liquid phase under moderate supercooling via direct molecular dynamics simulation. Square-lattice-like geometric order is observed at the early stage of nucleation and is preserved during the entire nucleus growth process. The diffusion coefficient and free energy profile in the cluster space extracted from a Bayesian trajectory analysis agree well with the classical nucleation theory (CNT) prediction and yield thermodynamic quantities exhibiting linear temperature dependence. The effectiveness of maximum cluster size as the descriptor of ice nucleation dynamics in the CNT framework can be attributed to the dynamical time scale decoupling and strong structural pattern dependence of density fluctuation in the liquid phase.

The ubiquitous existence of water confined in nano-capillaries is key to fundamental biological and technological applications. Using molecular dynamics simulations, we analyzed the hopping-like interlayer relocation dynamics of bilayer water confined between two parallel graphene sheets. In contrary to the common scheme that relocation is driven by density fluctuations, analysis of the transition path ensemble revealed that interlayer hopping is induced by local hydrogen bond configuration fluctuations coupled with activated consecutive transient angular reorientations. Our results consolidated the anisotropic nature of water relocation under strong ordering, which provided a mechanistic insight into the relaxation behavior at glass-forming water’s fragile-to-strong crossover.

As genetic material, DNA not only carries genetic information by sequence, but also affects biological functions ranging from base modification to replication, transcription and gene regulation through its structural and dynamic properties and variations. The motion and structural properties of DNA involved in related biological processes are also multi-scale, ranging from single base flipping to local DNA deformation, TF binding, G-quadruplex and i-motif formation, TAD establishment, compartmentalization and even chromosome territory formation, just to name a few. The sequence-dependent physical properties of DNA play vital role in all these events, and thus it is interesting to examine how simple sequence information affects DNA and the formation of the chromatin structure in these different hierarchical orders. Accordingly, molecular simulations can provide atomistic details of interactions and conformational dynamics involved in different biological processes of DNA, including those inaccessible by current experimental methods. In this perspective, which is mainly based on our recent studies, we provide a brief overview of the atomistic simulations on how the hierarchical structure and dynamics of DNA can be influenced by its sequences, base modifications, environmental factors and protein binding in the context of the protein–DNA interactions, gene regulation and structural organization of chromatin. We try to connect the DNA sequence, the hierarchical structures of DNA and gene regulation., An overview of atomistic simulations on the effect of sequences, base modifications, environmental factors and protein binding on DNA’s hierarchical structure and dynamics in the context of protein–DNA interactions, gene regulation and structural organization of chromatin.

Spontaneous mutations introduce uncertainty into COVID-19 control procedures and vaccine development. Here, we perform spatio-temporal analysis on intra-host single-nucleotide variations (iSNVs) in 402 clinical samples from 170 patients, which reveals an increase in genetic diversity over time post-symptoms onset within individual patients. Nonsynonymous mutations are over-represented in the pool of iSNVs, but underrepresent at the single nucleotide polymorphism (SNP) level, suggesting a two-step fitness selection process: a large number of nonsynonymous substitutions are generated within the host (positive selection), and these substitutions tend to be unfixed as SNPs in population (negative selection). Dynamic iSNVs changes in subpopulations of different gender, age, illness severity and viral shedding time displayed a varied fitness selection process among populations. Taken together, our study highlights iSNVs provide a mutational pool shaping the virus rapid global evolution., Graphical Abstract, The Intra-host variations in SARS-CoV-2 provide a mutational pool shaping the virus rapid global evolution. Here, Li et al. illustrate dynamic changes of iSNVs in a longitudinal cohort, and explored a two-step fitness selection process of within-host viral evolution.

To understand the microsolvation of alkaline-earth dihalides in water and provide information about the dependence of solvation processes on different halides, we investigated CaBr

Chromatin undergoes drastic structural organization and epigenetic reprogramming during embryonic development. We present here a consistent view of the chromatin structural change, epigenetic reprogramming, and the corresponding sequence-dependence in both mouse and human embryo development. The two types of domains, identified earlier as forests (CGI-rich domains) and prairies (CGI-poor domains) based on the uneven distribution of CGI in the genome, become spatially segregated during embryonic development, with the exception of zygotic genome activation (ZGA) and implantation, at which point significant domain mixing occurs. Structural segregation largely coincides with DNA methylation and gene expression changes. Genes located in mixed prairie domains show proliferation and ectoderm differentiation-related function in ZGA and implantation, respectively. The chromatin of the ectoderm shows the weakest and the endoderm the strongest domain segregation in germ layers. This chromatin structure difference between different germ layers generally enlarges upon further differentiation. The systematic chromatin structure establishment and its sequence-based segregation strongly suggest the DNA sequence as a possible driving force for the establishment of chromatin 3D structures that profoundly affect the expression profile. Other possible factors correlated with or influencing chromatin structures, including transcription, the germ layers, and the cell cycle, are discussed for an understanding of concerted chromatin structure and epigenetic changes in development.

The recent development of sequencing technology and imaging methods has provided an unprecedented understanding of the inter-phase chromatin folding in mammalian nuclei. It was found that chromatin folds into topological-associated domains (TADs) of hundreds of kilo base pairs (kbps), and is further divided into spatially segregated compartments (A and B). The compartment B tends to be located near to the periphery or the nuclear center and interacts with other domains of compartments B, while compartment A tends to be located between compartment B and interacts inside the domains. These spatial domains are found to highly correlate with the mosaic CpG island (CGI) density. High CGI density corresponds to compartments A and small TADs, and vice versa. The variation of contact probability as a function of sequential distance roughly follows a power-law decay. Different chromosomes tend to segregate to occupy different chromosome territories. A model that can integrate these properties at multiple length scales and match many aspects is highly desired. Here, we report a DNA-sequence based coarse-grained block copolymer model that considers different interactions between blocks of different CGI density, interactions of TAD formation, as well as interactions between chromatin and the nuclear envelope. This model captures the various single-chromosome properties and partially reproduces the formation of chromosome territories.

In order to understand the hydration processes of BaCl

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, theaccessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systemscontaining rare events remains challenging. Two distinct strategies are usually adopted in this regard: eithersticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveragingcoarse-grained models. Although both strategies are promising, either of them, if adopted individually,exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies sothat simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FGsimulations can be boosted by the guidance of CG models via deep reinforcement learning. Our methoddefines a variational and adaptive training objective which allows end-to-end training of parametricmolecular models using deep neural networks. Through multiple experiments, we show that our method isefficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Mammalian DNA replication is initiated at numerous replication origins, which are clustered into thousands of replication domains (RDs) across the genome.However, it remains unclear whether thereplication origins within each RDare activatedstochastically or preferentially near certain chromatin features. To understand how DNA replication in single human cells is regulated at the sub-RD level, we directly visualized and quantitatively characterized the spatio-temporal organization, morphology, andin situepigenetic signatures of individual replication foci (RFi) across S-phase at super-resolution using stochastic optical reconstruction microscopy. Importantly, we revealed a hierarchical radial pattern of RFi propagation dynamics that reverses directionality from early to late S-phase, and is diminished upon caffeine treatment or CTCF knockdown. Together with simulation and bioinformatic analyses, our findings point to a “CTCF-organized REplication Propagation” (CoREP) model, which suggests a non-random selection mechanism for replication activation at the sub-RD level during early S-phase, mediated by CTCF-organized chromatin structures. Collectively, these findings shed critical insights into the key involvement of local epigenetic environment in coordinating DNA replication across the genome, and have broad implications for our conceptualization of the role of multi-scale chromatin architecture in regulating diverse cell nuclear dynamics in space and time.

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, theaccessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systemscontaining rare events remains challenging. Two distinct strategies are usually adopted in this regard: eithersticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveragingcoarse-grained models. Although both strategies are promising, either of them, if adopted individually,exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies sothat simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FGsimulations can be boosted by the guidance of CG models via deep reinforcement learning. Our methoddefines a variational and adaptive training objective which allows end-to-end training of parametricmolecular models using deep neural networks. Through multiple experiments, we show that our method isefficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models; instead, we introduce various useful concepts and ideas brought by modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we give an outlook for promising directions which may help address the existing issues in the current framework of deep molecular modeling.

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to take advantage of both strategies. In this approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

It describes how hierarchical CpG distribution on the genomes of species change in the evolution and the correlation with the chromatin structure., Dinucleotide densities and their distribution patterns vary significantly among species. Previous studies revealed that CpG is susceptible to methylation, enriched at topologically associating domain boundaries and its distribution along the genome correlates with chromatin compartmentalization. However, the multi-scale organizations of CpG in the linear genome, their role in chromatin organization, and how they change along the evolution are only partially understood. By comparing the CpG distribution at different genomic length scales, we quantify the difference between the CpG distributions of different species and evaluate how the hierarchical uneven CpG distribution appears in evolution. The clustering of species based on the CpG distribution is consistent with the phylogenetic tree. Interestingly, we found the CpG distribution and chromatin structure to be correlated in many different length scales, especially for mammals and avians, consistent with the mosaic CpG distribution in the genomes of these species.

The three-dimensional (3D) chromatin structure, together with DNA methylation and other epigenetic marks, profoundly affects gene expression and displays abnormal behaviors in cancer cells. We elucidated the chromatin architecture remodeling in carcinogenesis from the perspective of spatial interactions between CGI forest and prairie domains, which are two types of megabase-sized domains defined by different sequence features but show distinct epigenetic and transcriptional patterns. DNA sequence strongly affects chromosome spatial interaction, DNA methylation and gene expression. Globally, forests and prairies show enhanced spatial segregation in cancer cells and such structural changes are accordant with the alteration of CGI interactions and domain boundary insulation, which could affect vital cancer-related properties. As the cancer progresses, a gradual increase of the DNA methylation difference between the two types of DNA domains is also observed for many different types of cancers. These observations are consistent with the change of transcriptional level differences of genes in these two domains, suggesting a highly-connected global structural, epigenetic and transcriptional activity changes in carcinogenesis.

The emergence of unnatural DNA bases provides opportunities to demystify the mechanisms by which DNA polymerases faithfully decode chemical information on the template. It was previously shown that two unnatural cytosine bases (termed "M-fC" and "I-fC"), which are chemical labeling adducts of the epigenetic base 5-formylcytosine, can induce C-to-T transition during DNA amplification. However, how DNA polymerases recognize such unnatural cytosine bases remains enigmatic. Herein, crystal structures of unnatural cytosine bases pairing to dA/dG in the KlenTaq polymerase-host-guest complex system and pairing to dATP in the KlenTaq polymerase active site were determined. Both M-fC and I-fC base pair with dA/dATP, but not with dG, in a Watson-Crick geometry. This study reveals that the formation of the Watson-Crick geometry, which may be enabled by the A-rule, is important for the recognition of unnatural cytosines.

We employ site-specific, linear and nonlinear infrared spectroscopic techniques as well as fluorescence spectroscopy and molecular dynamics simulations to investigate the binding interactions of urea and three of its derivatives, methylurea, 1,3-dimethylurea, and tetramethylurea, with protein aromatic and polar side chains. We find that (1) urea methylation leads to preferential interactions between the cosolvent molecules and aromatic side chains with an affinity that increases with the number of methyl groups; (2) interactions with tetramethylurea cause significant dehydration of aromatic side chains and the effect is most pronounced for tryptophan; and (3) while neither urea nor tetramethylurea shows preferential accumulation around a polar side chain, the number of hydrogen-bond donors around this side chain is significantly decreased in the presence of tetramethylurea. Taken together, our findings suggest that these urea derivatives, especially tetramethylurea, can effectively disrupt hydrophobic interactions in proteins. Additionally, tetramethylurea can promote intramolecular hydrogen-bond formation and hence induce α-helix folding in peptides, as observed.

Ion hydration and transport at interfaces are relevant to a wide range of applied fields and natural processes1–5. Interfacial effects are particularly profound in confined geometries such as nanometre-sized channels6–8, where the mechanisms of ion transport in bulk solutions may not apply9,10. To correlate atomic structure with the transport properties of hydrated ions, both the interfacial inhomogeneity and the complex competing interactions among ions, water and surfaces require detailed molecular-level characterization. Here we constructed individual sodium ion (Na+) hydrates on a NaCl(001) surface by progressively attaching single water molecules (one to five) to the Na+ ion using a combined scanning tunnelling microscopy and noncontact atomic force microscopy system. We found that the Na+ ion hydrated with three water molecules diffuses orders of magnitude more quickly than other ion hydrates. Ab initio calculations revealed that such high ion mobility arises from the existence of a metastable state, in which the three water molecules around the Na+ ion can rotate collectively with a rather small energy barrier. This scenario would apply even at room temperature according to our classical molecular dynamics simulations. Our work suggests that anomalously high diffusion rates for specific hydration numbers of ions are generally determined by the degree of symmetry match between the hydrates and the surface lattice. A sodium ion hydrated with three (rather than one, two, four or five) water molecules diffuses orders of magnitude more quickly than the other ion hydrates owing to the interfacial symmetry mismatch.

Cyclic dinucleotides (CDNs) present thousand-fold differences of dissociation constants to STING, a pivotal protein in cytosolic dsDNA immunity. To understand how subtle chemical changes in CDNs lead to these substantial variances, a precise ranking of binding affinity is needed. However, the large size and flexibility of CDNs elevate the entropic effect and pose a challenge for this precise prediction. Therefore, in this paper, we developed a new protocol, a combination of selective-integrated tempering sampling of ligands and molecular docking, to take into account the entropic effects originating from extensive ligand configurational space and solvation on binding affinity evaluations. The calculated ranking orders of CDNs and CDN-derivatives to wild type STING and R232H mutant are in agreement with experimental measurements. Further molecular dynamics analysis revealed that the interaction between phosphonate groups and 232R differentiates the binding affinities. The 2'-5' linked phosphonate groups have a larger tendency to form hydrogen bonds with 232R than those with 3'-5' linkages. Moreover, the new protocol identified structural features that enhanced CDNs-STING binding, such as anti-glycosidic bonds and large pro-R distances, which explains the high binding affinity of dithio-RpRp-2'3'-CDA to STING and is expected to provide valuable guidance in the lead-drug optimization.

Efficient sampling in both configuration and trajectory spaces, combined with mechanism analyses via data mining, allows a systematic investigation of the thermodynamics, kinetics, and molecular-detailed dynamics of chemical reactions in solution. Through a Bayesian learning algorithm, the reaction coordinate(s) of a (retro-)Claisen rearrangement in bulk water was variationally optimized. The bond formation/breakage was found to couple with intramolecular charge separation and dipole change, and significant dynamic solvent effects manifest, leading to the “in-water” acceleration of Claisen rearrangement. In addition, the vibrational modes of the reactant and the solvation states are significantly coupled to the reaction dynamics, leading to heterogeneous and oscillatory reaction paths. The calculated reaction rate is well interpreted by the Kramers’ theory with a diffusion term accounting for solvent–solute interactions. These findings demonstrated that the reaction mechanisms can be complicated in homogeneous solutions since the solvent–solute interactions can profoundly influence the reaction dynamics and the energy transfer process., An efficient sampling protocol allows direct simulations of chemical reactions without preassumption of reaction pathways. Machine learning algorithms further extract rich dynamic observations of transition events beyond transition state theory.

Subtle changes in protein sequences are able to alter ligand–protein interactions. Unraveling the mechanism of such phenomena is important for understanding ligand–protein interactions, including the DMXAA–STING interaction. DMXAA specifically binds to mouse STING instead of human STING. However, the S162A mutation and a newly discovered E260I mutation endow human STINGAQ with DMXAA sensitivity. Through molecular dynamics simulations, we revealed how these single mutations alter the DMXAA–STING interaction. Compared to mutated systems, structural correlations in the interaction of STINGAQ with DMXAA are stronger, and the correlations are cross-protomers in the dimeric protein. Analyses on correlation coefficients lead to the identification of two key interactions that mediate the strong cross-protomer correlation in the DMXAA–STINGAQ interaction network: DMXAA–267T–162S* and 238R–260E*. These two interactions are partially and totally interrupted by the S162A and E260I mutations, respectively. Moreover, a...

Repetitive cytosine rich i-motif forming sequences are abundant in the telomere, centromere and promoters of several oncogenes and in some instances are known to regulate transcription and gene expression. The in vivo existence of i-motif structures demands further insight into the factors affecting their formation and stability and development of better understanding of their gene regulatory functions. Most prior studies characterizing the conformational dynamics of i-motifs are based on i-motif forming synthetic constructs. Here, we present a systematic study on the stability and structural properties of biologically relevant i-motifs of telomeric and centromeric repeat fragments. Our results based on molecular dynamics simulations and quantum chemical calculations indicate that along with base pairing interactions within the i-motif core the overall folded conformation is associated with the stable C–H⋯O sugar “zippers” in the narrow grooves and structured water molecules along the wide grooves. The stacked geometry of the hemi-protonated cytosine pairs within the i-motif core is mainly governed by the repulsive base stacking interaction. The loop sequence can affect the structural dynamics of the i-motif by altering the loop motion and backbone conformation. Overall this study provides microscopic insight into the i-motif structure that will be helpful to understand the structural aspect of mechanisms of gene regulation by i-motif DNA.

Chromatin mainly consists of protein and DNA, and the sequence information of DNA contributes to controlling the spatial structure of chromatin. Genome-wide contact patterns of chromosome at high precision uncover fine structural properties, conductive to exploring underlying mechanisms on structure establishment and function realization for chromatin. In this short review, we describe changes of chromatin structure during various biological processes from a DNA sequence view, with an increase of the overall domain segregation from birth to senescence and establishment of cell identity related cross-domain contacts. Segregation patterns vary with cell stage and genomic distance. Meanwhile, possible effects of cell cycle, temperature, nuclear lamina and nucleolus on chromatin structure are discussed. At last, important roles of transcription factors and other proteins in proper chromatin organization are also discussed.

Chemical reactions can be strongly influenced by an external electric field (EEF), but because the EEF is often time-dependent and in case it does not adapt quickly enough to the reaction progress, especially during fast barrier crossing processes, dynamic effects could be important. Here we find that electrostatic interactions can reduce the height of the reaction barrier for a Claissen rearrangement reaction and accelerate the key motions for bonding. Meanwhile, strong electrostatic interactions can modify the barrier into an effective potential well, confining the system into the barrier until solvents adjust themselves to provide an appropriate EEF for charge redistribution. In this case, the otherwise concerted mechanism becomes a stepwise one. Consequently, the motion of solvents modulates the reaction dynamics and leads to heterogeneous reaction paths, even in a seemingly homogeneous aqueous solution. In addition, an excessive stabilization of transition state retards the barrier crossing process, making the thermodynamically favorable pathway less favored dynamically.

We study, in this paper, the physical properties of water confined between two parallel graphene plates with different slit widths to understand the effects of confinement on the water structure and how bulk properties are reached as the water layer thickens. It was found that the microscopic structures of the interfacial liquid layer close to graphene vary with the slit width. Water tends to locate at the center of the six-membered ring of graphene planes to form triangular patterns, as found by others. The narrower the slit width is, the more pronounced this pattern is, except for the slit width of 9.5 A, for which a well-defined two-layer structure of water forms. On the other hand, squared structures can be clearly seen in single snapshots at small (6.5 A and 7.5 A) but not large slit widths. Even at small slit widths, the square-like geometry is observed only when an average is taken for a short trajectory, and averaging over a long time yields a triangular pattern dictated by the graphene geometry. We estimate the length of time needed to observe two patterns, respectively. We also used the two-phase thermodynamic model to study the variation of entropy of confined water and found that at 8.5 A, the entropy of confined water is larger than that of bulk water. The rotational entropy of confined water is higher than that of bulk water for all slit widths due to the reduction of the hydrogen bond in the confined space.

Continuous band structure tuning, e.g., doping with different atoms, is one of the most important features of inorganic semiconductors. However, this can hardly be realized in organic semicondutors. Here, we report the first example of fine-tuning organic semiconductor band structures by alloying structurally similar derivatives into one single phase. By incorporating halogen atoms on different positions of the backbone, BDOPV derivatives with complementary intramolecular or intermolecular charge distributions were obtained. To maximize the Coloumbic attractive interactions and minimize repulsive interactions, they form antiparallel cofacial stacking in monocomponent or in alloy single crystals, resulting in efficient π orbital overlap. Benefiting from self-assembly induced solid state "olefin metathesis" reaction, it was observed, for the first time, that three BDOPV derivatives cocrystallized in one single crystal. Molecules with different energy levels serve like the dopants in inorganic semiconductors. Consequently, as the total number of halogen atoms increased, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels of the alloy single crystals decreased monotonously in the range from -5.94 to -6.96 eV and -4.19 to -4.48 eV, respectively.

Using an engineered pyrrolysyl-tRNA synthetase mutant together with [Formula: see text] , we have genetically encoded N(ε)-(7-azidoheptanoyl)-L-lysine (AzHeK) by amber codon in Escherichia coli for recombinant expression of a number of AzHeK-containing histone H3 proteins. We assembled in vitro acyl-nucleosomes from these recombinant acyl-H3 histones. All these acyl-nucleosomes contained an azide functionality that allowed quick click labeling with a strained alkyne dye for in-gel fluorescence analysis. Using these acyl-nucleosomes as substrates and click labeling as a detection method, we systematically investigated chromatin deacylation activities of SIRT7, a class III NAD(+)-dependent histone deacylase with roles in aging and cancer biology. Besides confirming the previously reported histone H3K18 deacylation activity, our results revealed that SIRT7 has an astonishingly high activity to catalyze deacylation of H3K36 and is also catalytically active to deacylate H3K37. We further demonstrated that this H3K36 deacylation activity is nucleosome dependent and can be significantly enhanced when appending the acyl-nucleosome substrate with a short double-stranded DNA that mimics the bridging DNA between nucleosomes in native chromatin. By overexpressing SIRT7 in human cells, we verified that SIRT7 natively removes acetylation from histone H3K36. Moreover, SIRT7-deficient cells exhibited H3K36 hyperacetylation in whole cell extracts, at rDNA sequences in nucleoli, and at select SIRT7 target loci, demonstrating the physiologic importance of SIRT7 in determining endogenous H3K36 acetylation levels. H3K36 acetylation has been detected at active gene promoters, but little is understood about its regulation and functions. Our findings establish H3K36 as a physiologic substrate of SIRT7 and implicate this modification in potential SIRT7 pathways in heterochromatin silencing and genomic stability.

Solvation of salts in water is a fundamental physical chemical process, but the underlying mechanism remains unclear. We investigated the contact ion pair (CIP) to solvent-separated ion pair (SSIP) transition in NaCl(H2O)n clusters with anion photoelectron spectroscopy and ab initio calculations. It is found that the SSIP type of structures show up at n = 2 for NaCl–(H2O)n anions. For neutral NaCl(H2O)n, the CIP structures are dominant at n < 9. At n = 9–12, the CIP structures and SSIP structures of NaCl(H2O)n are nearly degenerate in energy, coincident to the H2O:NaCl molar ratio of NaCl saturated solution and implying that the CIP and SSIP structures can coexist in concentrated solutions. These results are useful for understanding the solvation of salts at the molecular level.

NEIL1 (Nei-like 1) is a DNA repair glycosylase guarding the mammalian genome against oxidized DNA bases. As the first enzymes in the base-excision repair pathway, glycosylases must recognize the cognate substrates and catalyze their excision. Here we present crystal structures of human NEIL1 bound to a range of duplex DNA. Together with computational and biochemical analyses, our results suggest that NEIL1 promotes tautomerization of thymine glycol (Tg)-a preferred substrate-for optimal binding in its active site. Moreover, this tautomerization event also facilitates NEIL1-catalyzed Tg excision. To our knowledge, the present example represents the first documented case of enzyme-promoted tautomerization for efficient substrate recognition and catalysis in an enzyme-catalyzed reaction.

Cyclic dinucleotides are able to trigger the innate immune system by activating STING. It was found that the binding affinity of asymmetric 2'3'-cGAMP to symmetric dimer of STING is 3 orders of magnitude higher than that of the symmetric 3'3'-cyclic dinucleotides. Such a phenomenon has not been understood yet. Here we show that the subtle changes in phosphodiester linkage of CDNs lead to their distinct structural properties which correspond to the varied binding affinities. 2'-5' and/or 3'-5' linked CDNs adopt specific while different types of ribose puckers and backbone conformations. That ribose conformations and base types have different propensities for anti or syn glycosidic conformations further affects the overall flexibility of CDNs. The counterbalance between backbone ring tension and electrostatic repulsion, both affected by the ring size, also contributes to the different flexibility of CDNs. Our calculations reveal that the free energy cost for 2'3'-cGAMP to adopt the STING-bound structure is smaller than that for 3'3'-cGAMP and cyclic-di-GMP. These findings may serve as a reference for design of CDN-analogues as vaccine adjuvants. Moreover, the cyclization pattern of CDNs closely related to their physiological roles suggests the importance of understanding structural properties in the study of protein-ligand interactions.

Urea is known for protein denaturation. The counteracting effect of trimethylamine-N-oxide (TMAO) against urea-induced protein denaturation is also well established. However, what is largely unknown is the mechanism TMAO counteracts urea. In this article, the hydration of the interior of a simple single-walled carbon nanotube in a urea/TMAO mixture is studied as a model system for hydrophobic hydration using molecular dynamic simulations. The results show that TMAO counteracts the hydration effect of urea to the nanotube interior through strong interactions among TMAO, water, and urea. The strong interactions of TMAO and water stabilize the water structure, which counteracts the effects of urea indirectly.

Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper, we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; in turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible and performs well on challenging chemical and bio-molecular systems.

In order to understand the hydration processes of BaCl2, we investigated BaCl2(H2O)n− (n = 0–5) clusters using size-selected anion photoelectron spectroscopy and theoretical calculations. The structures of neutral BaCl2(H2O)n clusters up to n = 8 were also investigated by theoretical calculations. It is found that in BaCl2(H2O)n−/0, the Ba–Cl distances increase very slowly with the cluster size. The hydration process is not able to induce the breaking of a Ba–Cl bond in the cluster size range (n = 0–8) studied in this work. In small BaCl2(H2O)n clusters with n ≤ 5, the Ba atom has a coordination number of n + 2; however, in BaCl2(H2O)6–8 clusters, the Ba atom coordinates with two Cl atoms and (n − 1) water molecules, and it has a coordination number of n + 1. Unlike the previously studied MgCl2(H2O)n− and CaCl2(H2O)n−, negative charge-transfer-to-solvent behavior has not been observed for BaCl2(H2O)n−, and the excess electron of BaCl2(H2O)n− is mainly localized on the Ba atom rather on the water molecules. No observation of Ba2+–Cl− separation in current work is consistent with the lower solubility of BaCl2 compared to MgCl2 and CaCl2. Considering the BaCl2/H2O mole ratio in the saturated solution, one would expect that about 20–30 H2O molecules are needed to break the first Ba–Cl bond in BaCl2.

In this Letter, the links to Supplementary Videos 5, 7, 9 and 10 were incorrect, and there were some formatting errors in the Supplementary Video legends. These errors have been corrected online.

To understand the initial hydration processes of CaCl2, we performed molecular simulations employing the force field based on the theory of electronic continuum correction with rescaling. Integrated tempering sampling molecular dynamics were combined with ab initio calculations to overcome the sampling challenge in cluster structure search and refinement. The calculated vertical detachment energies of CaCl2(H2O)n- (n = 0-8) were compared with the values obtained from photoelectron spectra, and consistency was found between the experiment and computation. Separation of the Cl-Ca ion pair is investigated in CaCl2(H2O)n- anions, where the first Ca-Cl ionic bond required 4 water molecules, and both Ca-Cl bonds are broken when the number of water molecules is larger than 7. For neutral CaCl2(H2O)n clusters, breaking of the first Ca-Cl bond starts at n = 5, and 8 water molecules are not enough to separate the two ion pairs. Comparing with the observations on magnesium chloride, it shows that separating one ion pair in CaCl2(H2O)n requires fewer water molecules than those for MgCl2(H2O)n. Coincidentally, the solubility of calcium chloride is higher than that of magnesium chloride in bulk solutions.

The high-order chromatin structure plays a non-negligible role in gene regulation. However, the mechanism for the formation of different chromatin structures in different cells and the sequence dependence of this process remain to be elucidated. As the nucleotide distributions in human and mouse genomes are highly uneven, we identified CGI forest and prairie genomic domains based on CGI density, which better segregates genomic elements along the genome than GC content. The genome is then divided into two sequentially, epigenetically, and transcriptionally distinct regions. These two types of megabase-sized domains spatially segregate, but to a different extent in different cell types. Overall, the forests and prairies gradually segregate from each other in development, differentiation, and senescence. The multi-scale forest-prairie spatial intermingling is cell-type specific and increases in differentiation, thus helps define the cell identity. We propose that the phase separation of the 1D mosaic sequence in space, serving as a potential driving force, together with cell type specific epigenetic marks and transcription factors, shapes the chromatin structure in different cell types and renders them distinct genomic properties. The mosaicity of the genome manifested in terms of alternative forests and prairies of a species could be related to its biological processes such as differentiation, aging and body temperature control.

High potential energy barriers and engagement of solvent coordinates set challenges for in silico studies of chemical reactions, and one is quite commonly limited to study reactions along predefined reaction coordinate(s). A systematic protocol, QM/MM MD simulations using enhanced sampling of reactive trajectories (ESoRT), is established to quantitatively study chemical transitions in complex systems. A number of trajectories for Claisen rearrangement in water and toluene were collected and analyzed, respectively. Evidence was found that the bond making and breaking during this reaction are concerted processes in solutions, preferentially through a chairlike configuration. Water plays an important dynamic role that helps stabilize the transition sate, and the dipole-dipole interaction between water and the solute also lowers the transition barrier. The calculated rate coefficient is consistent with the experimental measurement. Compared with water, the reaction pathway in toluene is "narrower" and the reaction rate is slower by almost three orders of magnitude due to the absence of proper interactions to stabilize the transition state. This study suggests that the "in-water" nature of the Claisen rearrangement in aqueous solution influences its thermodynamics, kinetics, as well as dynamics.

Recent single-molecule measurements have revealed the DNA allostery in protein/DNA binding. MD simulations showed that this allosteric effect is associated with the deformation properties of DNA. In this study, we used MD simulations to further investigate the mechanism of DNA structural correlation, its dependence on DNA sequence, and the chemical modification of the bases. Besides a random sequence, poly d(AT) and poly d(GC) are also used as simpler model systems, which show the different bending and twisting flexibilities. The base-stacking interactions and the methyl group on the 5-carbon site of thymine causes local structures and flexibility to be very different for the two model systems, which further lead to obviously different tendencies of the conformational deformations, including the long-range allosteric effects.

ConspectusDespite great advances in molecular dynamics simulations, there remain large gaps between the simulations and experimental observations in terms of the time and length scales that can be approached. Developing fast and accurate algorithms and methods is of ultimate importance to bridge these gaps. In this Account, we briefly summarize recent efforts in such directions. In particular, we focus on integrated tempering sampling. The efficiency of this sampling method has been demonstrated by applications to a range of chemical and biological problems: protein folding, molecular cluster structure searches, and chemical reactions. The combination of integrated tempering sampling and a trajectory sampling method allows the calculation of rate constants and reaction pathways without predefined collective coordinates.

The Li(H2O)n– and Cs(H2O)n– (n = 0–6) clusters were studied using anion photoelectron spectroscopy combined with ab initio calculations. It was found that Li tends to be surrounded by water molecules with no water–water H-bonds being formed in the first hydration shell; while Cs sticks on the surface of water–water H-bonds network. The Li atom in its anionic or neutral state is surrounded by four water molecules through Li–O interactions within the first hydration shell; while the case of Cs is different. For the anionic Cs(H2O)n– clusters, two types of structures, namely H-end and O-end structures, were identified, with nearly degenerate energies. For the neutral Cs(H2O)n clusters, only O-end structures exist and the first hydration shell of the Cs atom has four water molecules. The different hydration nature of Li and Cs atoms can be ascribed to the delicate balance between the alkali metal–water interactions and the water–water interactions as well as the effect of excess electron.

The relationship between DNA methylation and chromatin structure is still largely unknown. By analyzing a large set of published sequencing data, we observed a long-range power law correlation of DNA methylation with cell class-specific scaling exponents in the range of tens of kilobases. We showed that such cell class-specific scaling exponents are caused by different patchiness of DNA methylation in different cells. By modeling the chromatin structure using high-resolution chromosome conformation capture data and mapping the methylation level onto the modeled structure, we demonstrated that the patchiness of DNA methylation is related to chromatin structure. The scaling exponents of the power law correlation are thus a display of the spatial organization of chromatin. Besides the long-range correlation, we also showed that the local correlation of DNA methylation is associated with nucleosome positioning. The local correlation of partially methylated domains is different from that of nonpartially methylated domains, suggesting that their chromatin structures differ at the scale of several hundred base pairs (covering a few nucleosomes). Our study provides a novel, to our knowledge, view of the spatial organization of chromatin structure from a perspective of DNA methylation, in which both long-range and local correlations of DNA methylation along the genome reflect the spatial organization of chromatin.